EP1345162A2 - Zeichenerkennungsvorrichtung und -verfahren - Google Patents

Zeichenerkennungsvorrichtung und -verfahren Download PDF

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Publication number
EP1345162A2
EP1345162A2 EP03075708A EP03075708A EP1345162A2 EP 1345162 A2 EP1345162 A2 EP 1345162A2 EP 03075708 A EP03075708 A EP 03075708A EP 03075708 A EP03075708 A EP 03075708A EP 1345162 A2 EP1345162 A2 EP 1345162A2
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character
vector
document
recited
class
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French (fr)
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EP1345162A3 (de
EP1345162B1 (de
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Jean-Pierre Polonowski
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IMDS Software Inc
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IMDS Software Inc
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • G06F18/24137—Distances to cluster centroïds

Definitions

  • the present invention relates to character and pattern recognition systems and methods.
  • OCR optical character recognition
  • OCR Optical Character Recognition
  • each character which needs to be recognized is considered to be a different class.
  • the recognition of a character includes the characterization of their features or patterns. While there are generally different views on the definition of the features of patterns, many studies made on the recognition of characters as well as on the recognition of patterns have shown that the so-called quasi-topological features of a character or pattern such as the concavity, loop, and connectivity are key features for the recognition. To date, many different methods have been proposed for the purpose of extracting such features. For example, some of these methods use analysis of the progressive slopes of the black pixels.
  • On-line handwriting recognition systems have been designed which compute feature vectors as functions of time.
  • An example of such systems is described in T. Starner, J. Makhoul, R. Schwartz and G. Chou; "On-Line Cursive Handwriting Recognition Using Speech Recognition Methods; IEEE International Conference on Acoustics, Speech, and Signal Processing, Sydney, Australia, Apr. 19-22, 1994, Vol. V. pp. 125-128.
  • on-line handwriting recognitions systems are not suitable for OCR applications since these applications are faced with the problem of recognizing a whole page of text which presents a two-dimensional problem for which there is no obvious way of defining a feature vector as a function of one independent variable.
  • Fuzzy logic was developed to enable data processors based on binary logic to provide an answer between "yes” and “no.”
  • Fuzzy logic is a logic system which has membership functions with fuzzy boundaries. Membership functions translate subjective expressions, such as "temperature is warm,” into a value which typical data processors can recognize.
  • a label such as "warm” is used to identify a range of input values whose boundaries are not points at which the label is true on one side and false on the other side. Rather, in a system which implements fuzzy logic, the boundaries of the membership functions gradually change and may overlap a boundary of an adjacent membership set. Therefore, a degree of membership is typically assigned to an input value. For example, given two membership functions over a range of temperatures, an input temperature may fall in the overlapping areas of both the functions labelled "cool” and "warm.” Further processing would then be required to determine a degree of membership in each of the membership functions.
  • Fuzzy logic control systems have become increasingly popular in practical applications.
  • the design of the knowledge base including membership functions and rules relies on a subjective human "rule-of-thumb" approach for decision-making.
  • the control system is adapted (tuned) to the desired performance through trial and error.
  • designing and adapting the fuzzy logic control system becomes a time-consuming task.
  • neural network techniques have been used in assisting designers to generate rules and adapt the fuzzy logic control system automatically.
  • a fuzzy logic system is inherently well-suited for dealing with imprecise data such as handwritten character and processing rules in parallel.
  • fuzzy rule-based systems for this type of application often relies on a substantial amount of heuristic observation to express the knowledge of the system.
  • each character is represented as one consequent of a rule.
  • fuzzy rule-based systems for this type of application often relies on a substantial amount of heuristic observation to express the membership functions for the antecedents of each rule.
  • Each rule consists of several antecedents and consequents depending on the number of inputs and outputs, respectfully.
  • Each antecedent in a given rule is defined as an input membership function, and each consequent is defined as an output membership function.
  • Neural networks consist of highly interconnected processing units that can learn and globally estimate input-output functions in a parallel-distribution framework. Fuzzy logic system store and process rules that output fuzzy sets associated with input fuzzy sets in parallel. The similar parallelism properties of neural nets and fuzzy logic systems have lead to their integration in studies of the behaviour of highly complex systems.
  • Neural networks are dependent on the exact sequence of «learning» of the knowledge base. If the same knowledge base is fed twice to a neural Network with only one substitution in the learning sequence, the end result will be different in each case. This can be a major disadvantage for any OCR system.
  • LVQ Learning Vector Quantization
  • LVQ can group similar input data into the same class by adjusting the connection weights between the inputs and their corresponding output. In other words, through supervised learning, the features of each class can be extracted from its associated inputs.
  • a learning vector quantization neural network may be used to optimize the features of each handwritten character.
  • Ming-Kuei Hu in “Visual Pattern Recognition Moment Invariant,” IEEE Transaction on Information Theory, pp. 179-186, 1962, describes such a system.
  • a LVQ network is also disclosed in Teuvo Kohonen, "The Self-Organizing Map,” Proceeding of the IEEE, Vol. 78, No. 9, pp. 1364-1479, September 1990.
  • a LVQ learning system can be seen as a two-layered network.
  • the first layer is the input layer; the second is the competitive layer, which is organized as a two-dimensional grid. All units (a "unit” is represented as one input variable, such as x1, of one input pattern (x1, x2.%)) from the first layer to the second are fully interconnected.
  • the units of the second layer are grouped into classes, each of which pertains to one character.
  • an input pattern consists of the values of each input variable and its corresponding class (i.e. the character that it represents).
  • a quantization unit in the competitive layer has an associated vector comprising the values of each interconnection from all the units in the input layer to itself. This vector implicitly defines an ideal form of character within a given class.
  • the LVQ learning system determines the class borders using a nearest-neighbour method. This method computes the smallest distance between the input vector X: (x1, x2, .. ; xn) and each quantization vector. In known systems, this computation is done in terms of Euclidean distance (straight line distance in multi-dimensional space).
  • Input vector X belongs to class C(x), and quantization vector w(I) belongs to class C(w). If C(x) and C(w) belong to different classes, the w(I) is pulled away from the class border to increase the classification accuracy. If C(x) and C(w) have the same class, the w(I) closes to the center of the class. Then each input pattern is presented sequentially in the input layer and several iterations. The weights of the quantization units in each class are fine-tuned to group around the center of the class. Therefore, the weight vector of the center unit within the class is represented as the optimum classification for the corresponding class.
  • the result of the LVQ learning process is an optimized vector for each alphanumeric character.
  • Multi-layer perceptron is a well known application of neural networks. This method can be excellent if great care is used in the training phase. However, because no theoretical base exist to improve the result in a structured way, one must rely on trial and error processes which are extremely costly. As a result, if the multi-layer perceptron system is "taught" the same data twice, two different results will be obtained.
  • the confusion rate is defined as the number of characters which were thought to have been recognized but were in fact wrongly recognized divided by the total number of characters read.
  • the rejection rate is the number of characters which the recognition method has failed to recognize over the total number of characters read.
  • the read rate consists in the total number of characters that were accurately read over the total number of characters read. Therefore, the read rate plus the confusion rate plus the rejection rate should equal 100%.
  • the read rate has to be high enough for the recognition system to be worthwhile. Therefore, the ideal system is the one in which the confusion rate is zero and the read rate is as close as possible to perfect. Limiting factors for the read rate include:
  • a method for translating a written document into a computer readable document comprising:
  • a method for recognizing a character corresponding to a written symbol comprising:
  • a method for creating a vector base for a character recognition method comprising:
  • a character recognition learning method comprising:
  • character should be construed as including any written symbols or shapes, including but not limiting to letters (in any alphabet), numbers, etc.
  • the system 10 comprises a controller 12, input devices such as a pointing device 14 and a keyboard 16, a display device 18, a document digitizer 20, and a storing device (not shown).
  • the input devices 14 and 16, and display device 18 are coupled to the controller 12 through conventional coupling means.
  • the display device 18 and input devices 14 and 16 are optional but may allow a system operator to perform the verification of unrecognized characters as will be explained hereinbelow in more detail.
  • the display device 18 is in the form of a computer monitor, but may alternatively be in the form of a liquid crystal display or of any device that allows display of digitized printed character or of the actual printed or handwritten characters as found on the document.
  • the pointing device 14 is in the form of a computer mouse.
  • a single input device may be used to operate the system 10 depending on the user interface programmed in the controller 12.
  • the document digitizer 20 includes an optical reader allowing to image at least part of the document 8 and to create a pixel or bitmap representation thereof.
  • the document digitizer 20 is configured so as to transform the image of document 8 into a binary document. Means for achieving this transformation may be included in the controller 12, for example in the form of a digitization module, or part of a device that includes the optical reader.
  • a conventional scanner may be used, commercial applications may require the use of both a camera, such as a CCD (charge-coupled device) camera, and a dedicated processing power of the controller 12.
  • a camera such as a CCD (charge-coupled device) camera
  • the controller 12 is advantageously in the form of a computer.
  • the controller 12 may alternatively take many forms such as an electronic control circuit or a programmed chip.
  • the storing device may take many form including: a disk, cdrw, or dvd drive, a hard disk, memory card, computer RAM (Read Only Memory), etc.
  • the storing device may be included in the controller, or be embodied in a device (not shown) coupled to the controller 12.
  • the controller 12 is advantageously programmed so as to embody a method 100 for translating a written document 8 into a computer readable document according to an embodiment of a second aspect of the present invention, as will now be described with reference to Figure 2.
  • the method 100 comprises the following steps:
  • step 110 the written document 8 to be translated into a computer-readable file is digitized using the system 10 and more specifically the digitizing device 20.
  • the result of the digitization step 110 is a pixel representation 22 of the document to be translated, which is exemplified in Figure 3.
  • the pixel representation 22 of the document may be either stored into the storing device (not shown) for later processing or be made available to the controller 12 for further processing.
  • a pixel representation of the document may be provided to the controller 12. This pixel representation may result from a prior independent segmentation process.
  • step 120 the type of document is determined. This step is optional, but allow to facilitate the next step (130) which is the separation of the digitized document 22 into fields 24 in those cases where many different types of documents may be read by the system 10.
  • Step 120 is achieved on the digitized document by identifying certain characteristics such as markings or a configuration that is expected for a pre-determined type of documents.
  • markings or configuration are pre-determined and stored, for example in the controller 12, to be used in step 120.
  • characteristics that can be used to identify a document type include: text block, dimension, lines.
  • the type of document is recognized by the operator and inputted in the controller.
  • step 130 the document is then separated into fields 24 (see Figure 3).
  • a field is a generally rectangular section of the document known to include predetermined information coded as a one or more characters. For example, on a bank related document, a bank account identifier is to be found in a predetermined portion of the document that includes characters identifying the account.
  • a field may have a different shape then a rectangle.
  • the segmentation algorithm (first sub-step of step 140) has to be adapted to the specific shape of the field.
  • the identification of fields in the pixel representation 22 of the document 8 is advantageous since, in most applications, one does not have to read every characters on the document 8. It is therefore more efficient (both time and money wise) to concentrate the character recognition process on fields having relevant information.
  • step 130 is a rectangular bitmap (see 24 on Figure 3).
  • step 140 the information in each field 24 is extracted. More specifically, the bitmap information 24 in an identified field (step 130) is translated into a predetermined computer-readable format such as ASCII or EBCDIC.
  • Step 140 includes the following two substeps:
  • segmentation substep takes into account the fact that size of the bitmap representation 26 of a character can vary considerably from one character to another and, in the field 24 including handwritten characters, from one writer to another.
  • step 140 concerns the actual character recognition. This operation seeks to associate a given digital character to each character image (symbol) 26 defined during the segmentation substep. A similarity score is associated to each predetermined possibility among the a list of possible digital characters. This operation presumes that each discriminated symbol resulting from the segmented process is the image of a single character.
  • the character recognition method 200 is determined and characterized by the following choices:
  • the character recognition method per se includes two substeps: the vector quantization (substep 202), and the vector classification (substeps 204-210). Substeps 204-210 are done using a vector base as will be explained herein in more detail.
  • a quantization vector is a mathematical representation including many components, each describing a characteristic of symbol or shape to be analyzed. These vectorial representations allow to recognize a character from a visually non-ambiguous shape. Many quantization vectors are known and can be used in the character recognition method according to the present invention. Substep 202 therefore yields a mathematical representation of each segmented bitmap character representations. Of course, the same quantization model has to be used for the character recognition process and for the vector base definition process.
  • a similarity score is computed by comparing the vector quantization of the current character to be recognized and predetermined vector quantization of each possible output among the possible predetermined valid characters computed with the same model. According to a most preferred embodiment of the present invention, the comparison is achieved using an optimal spherical separation method as will be described hereinbelow with reference to the learning process.
  • LDC optimal linear separation method
  • MLP multi layer perceptron
  • substep 206 the similarity score for each class is compared to a predetermined threshold.
  • the threshold is a function of scores which result from the learning base, and of the predetermined acceptable error rate. For example, if one notice that the characters "3" and “9" are confounded with scores of 0.5, thresholds should be increased above 0.5 for each character.
  • the comparison process consists in verifying if the vector characterisation of the current character to be recognized is close enough to one of the members populating each classes (each character which needs to be recognized is considered to be a different class).
  • step 208 If the similarity score has not reached the threshold or if more then one class threshold are reached (step 208), the character is not recognized and the method returns to the human inspection step (step 160 from Figure 2).
  • the ASCII code corresponding to the recognized character class is attributed (substep 210), and the method proceeds with the next segmented bitmap 26 (also called pixel cluster) representation in the current field 24.
  • the method proceeds with the verification of the character (step 150 from Figure 2).
  • the resulted vectors are then classified or separated into a number of classes each corresponding to one shape which is sought to be recognised.
  • Each such shape is usually one of the characters in the universe of valid characters in the application.
  • the character recognition method and system is preferably optimized for a specific use.
  • the field to be recognized is a numeric field
  • the class separation will be optimized to produce a digit from zero to nine and to exclude all others.
  • the different outputs and their associated scores are sorted by decreasing similarity scores. In those cases where one output does not have a similarity score which is high enough to determine the "winning" character, further processing is required. These steps will be described hereinbelow in more detail.
  • a character recognition method may comprise a number of recognition engines each being composed of a particular vector quantization and a class separation method as well as a voting method allowing the combination of the results of the various recognition engines using simple averages, weighted averages or veto rules.
  • step 150 the method 100 proceeds, in step 150, with the verification of the recognized characters.
  • This step consists in verifying the character obtained in step 140 by applying pre-determined field validity rules (for example the algorithm or key to validate a bank account number or a social security number).
  • pre-determined field validity rules for example the algorithm or key to validate a bank account number or a social security number.
  • other verification process may alternatively be used such as:
  • the verification step may consist in further deciding if the level of confidence on the character found is high enough to be considered recognized, or should the method 100 proceeds with the next step (160).
  • the verification step 150 may consist in better assessing the class of the character to be identified by analyzing it in the context of its neighbor characters.
  • an ICR Intelligent Character Recognition
  • the character is expected to be a letter part of a word
  • an ICR module advantageously included in the computer 12, may be used to identify the word and thereby the letters forming it. Since, ICR module are believed to be well known in the art, they will not be discussed herein in more detail.
  • step 160 unrecognized characters are verified by the operator of the system 10.
  • the operator reviews each field for which no similarity score is higher then the predetermined thresholds, and also, optionally those fields not corresponding to managing rules predetermined for the identified type of document.
  • Step 160 allows the operator to input any missing information following the automatic character recognition process. It is to be noted that this step allows also to correct errors resulting from the original input by a person.
  • a quality control is then optionally executed in step 170.
  • This step includes sampling some of the resulting recognized characters, and the operator visually verifying their validity by comparison with the corresponding initial character on the document 8.
  • sampling rate may vary depending on the application and the desired success rate.
  • sampled characters may be chosen randomly or systematically.
  • step 180 the computer-readable codes that have been assigned to the recognized characters (humanly or resulting from the character recognition method) are assembled an formatted so as to be displayed or stored for later display.
  • the vector base is a database including a plurality of quantization vector for each given class.
  • a learning process is used to create or to add to a base of vectors of known specimens of the vocabulary, each assigned to a given class.
  • a method of creation of a vector base will be described in more detail hereinbelow.
  • step 302 a plurality of characters or shapes are provided.
  • a bitmap (pixel) representation of each character or shape is then created in step 304.
  • This bitmap representation is then passed through the predetermined vector quantization method (step 306).
  • a similarity score similar to those described for the character recognition method 200, is then assigned for the character in respect of all classes (308). If the similarity score exceeds a predetermined threshold (310) it means the character is already known and can thus be ignored (312). If not, it is examined by an operator of the system 10 to determine if it belongs to the desired vocabulary (314). If not, it is rejected (316). If it belongs to the vocabulary, the current vector representation is stored to be added to the vector base and associated to the proper class (318) as assigned by the operator.
  • This last threshold is determined through experimentation and depends on the expected success rate. Indeed, a high threshold should be used to obtain a highly precise table, at the risk of rejecting numerous elements and of adding elements already present in the table.
  • the learning process 400 is completed as described in Figure 5.
  • the learning process described hereinbelow is based on the optimal elliptic separation method.
  • the learning process is as follows.
  • the shortest distance between the quantization vector representing the pattern and the closest quantization vector of another class is measured (402).
  • Many well-known mathematical methods may be used to compute the distance between these two vectors.
  • This distance is then used to define, for each class, a sphere which will comprise only vectors which are members of the same class (404). Therefore, the closed surface formed by this sphere separates all the members of the class contained in the sphere from all other classes.
  • the number of same class vectors contained in the sphere is determined and the database is sorted using such number (from the largest to the smallest) (406).
  • Reading hand-printed characters are particularly difficult because it is impossible to ever have a database of vectors representing every possible variation in handwritten characters.
  • the use of the optimal elliptical separation method considerably reduces the negative impact resulting from the use of "incomplete" vector databases.
  • searching all the various ellipsoids and retaining all of those which may relate to the proposed character and by assigning a similarity score it is possible to "learn" where the character belongs and make the appropriate adjustments to the various ellipsoids.
  • One way to assign a score to each particular member of an ellipsoid is to attribute a score of zero if the given character is located at the perimeter of the ellipsoid while attributing a score of one if such character is located at its "centre".
  • the appropriate scoring equation is: 1 -exp- ⁇ ( ⁇ i x i 2 ).
  • ⁇ i is the square of the radius of the sphere.
  • the learning base is re-examined to add all characters from the corresponding class that fit within the ellipsoid.
  • the predetermined percentage is preferably 99.5%. It has been found that a using a percentage of 99.5% is an acceptable compromised between efficiency of the learning process and the time required for the process.
  • the ⁇ i coefficients may be adjusted over time to yield vectors that better discriminate different characters and symbols.
  • the goal is to repeat the learning process each time a new character or symbol added to the learning base cause the classes to become linearly un-separable, i.e. the vectors do not allow to distinguish between two characters or symbols.
  • present methods and systems according to the present invention may be used to recognized both printed and handwritten characters or symbols.

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EP03075708A 2002-03-11 2003-03-11 Zeichenerkennungsvorrichtung und -verfahren Expired - Lifetime EP1345162B1 (de)

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CA002375355A CA2375355A1 (en) 2002-03-11 2002-03-11 Character recognition system and method
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EP1345162A3 EP1345162A3 (de) 2003-10-15
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CA2375355A1 (en) 2003-09-11

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